Overview

Dataset statistics

Number of variables13
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory106.0 B

Variable types

NUM13

Warnings

B2.1.1 Public Library Operating Grant (PLOG) is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
B2.1.4 Total Provincial Operating Funding is highly correlated with B2.1.1 Public Library Operating Grant (PLOG) and 10 other fieldsHigh correlation
B5.0 Total Operating Expenditures is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
B4.02.1 Total funds (not including employee benefits) is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
B4.04 Facilities/Utilities (Costs related to library facility operation, e.g. insurance, rent, lighting, maintenance, etc.) is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
B4.01.2 Electronic (e.g. electronic subscriptions and other databases, downloadable media, gaming software, Playaway, DVDs, and e-resources) is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
P2.2 Resident Households Served is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
C0.2.T Total Print Volumes Held is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
D1.1.1.C Librarians is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
G1.1.2.W All circulation for E-books, downloadable audio books, music and video is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
E7.1 In the space provided, please provide the total, combined square footage of all the facilities in your library system is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
Actual incidents is highly correlated with B2.1.4 Total Provincial Operating Funding and 10 other fieldsHigh correlation
df_index has unique values Unique
B4.02.1 Total funds (not including employee benefits) has 6 (9.4%) zeros Zeros
B4.04 Facilities/Utilities (Costs related to library facility operation, e.g. insurance, rent, lighting, maintenance, etc.) has 7 (10.9%) zeros Zeros
B4.01.2 Electronic (e.g. electronic subscriptions and other databases, downloadable media, gaming software, Playaway, DVDs, and e-resources) has 7 (10.9%) zeros Zeros
P2.2 Resident Households Served has 3 (4.7%) zeros Zeros
C0.2.T Total Print Volumes Held has 6 (9.4%) zeros Zeros
D1.1.1.C Librarians has 19 (29.7%) zeros Zeros
G1.1.2.W All circulation for E-books, downloadable audio books, music and video has 8 (12.5%) zeros Zeros
E7.1 In the space provided, please provide the total, combined square footage of all the facilities in your library system has 7 (10.9%) zeros Zeros

Reproduction

Analysis started2022-10-26 20:56:39.887063
Analysis finished2022-10-26 20:57:00.698130
Duration20.81 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.59375
Minimum13
Maximum396
Zeros0
Zeros (%)0.0%
Memory size512.0 B
2022-10-26T16:57:00.880710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile26.6
Q1103
median181.5
Q3296
95-th percentile363.7
Maximum396
Range383
Interquartile range (IQR)193

Descriptive statistics

Standard deviation110.7818848
Coefficient of variation (CV)0.5606548019
Kurtosis-1.178133798
Mean197.59375
Median Absolute Deviation (MAD)95.5
Skewness0.1071729971
Sum12646
Variance12272.62599
MonotocityStrictly increasing
2022-10-26T16:57:01.268404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25611.6%
 
25911.6%
 
6011.6%
 
15811.6%
 
28211.6%
 
31111.6%
 
34611.6%
 
19811.6%
 
7411.6%
 
33111.6%
 
Other values (54)5484.4%
 
ValueCountFrequency (%) 
1311.6%
 
2011.6%
 
2511.6%
 
2611.6%
 
3011.6%
 
ValueCountFrequency (%) 
39611.6%
 
39411.6%
 
36611.6%
 
36411.6%
 
36211.6%
 

B2.1.4 Total Provincial Operating Funding
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208041
Minimum3258
Maximum5573927
Zeros0
Zeros (%)0.0%
Memory size512.0 B
2022-10-26T16:57:01.511797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3258
5-th percentile5131.75
Q114617.5
median34048
Q3132022.25
95-th percentile569490.35
Maximum5573927
Range5570669
Interquartile range (IQR)117404.75

Descriptive statistics

Standard deviation718186.8054
Coefficient of variation (CV)3.45214071
Kurtosis51.40659179
Mean208041
Median Absolute Deviation (MAD)25970.5
Skewness6.914496465
Sum13314624
Variance5.157922874e+11
MonotocityNot monotonic
2022-10-26T16:57:01.781488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3251323.1%
 
1682423.1%
 
1672023.1%
 
10661123.1%
 
22758323.1%
 
36806923.1%
 
505023.1%
 
1952323.1%
 
3558323.1%
 
2792223.1%
 
Other values (44)4468.8%
 
ValueCountFrequency (%) 
325811.6%
 
483111.6%
 
505023.1%
 
559511.6%
 
677511.6%
 
ValueCountFrequency (%) 
557392711.6%
 
138032811.6%
 
94945111.6%
 
59882911.6%
 
40323811.6%
 

B2.1.1 Public Library Operating Grant (PLOG)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188615.7656
Minimum1719
Maximum5505920
Zeros0
Zeros (%)0.0%
Memory size512.0 B
2022-10-26T16:57:02.047117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1719
5-th percentile5050
Q113227
median33076
Q3111431.75
95-th percentile546384.9
Maximum5505920
Range5504201
Interquartile range (IQR)98204.75

Descriptive statistics

Standard deviation703098.0566
Coefficient of variation (CV)3.727673847
Kurtosis54.05682345
Mean188615.7656
Median Absolute Deviation (MAD)26276.5
Skewness7.142274607
Sum12071409
Variance4.943468772e+11
MonotocityNot monotonic
2022-10-26T16:57:02.322414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16059523.1%
 
3394423.1%
 
22073223.1%
 
505023.1%
 
2543323.1%
 
1862523.1%
 
1672023.1%
 
1664623.1%
 
3220823.1%
 
10176423.1%
 
Other values (44)4468.8%
 
ValueCountFrequency (%) 
171911.6%
 
305811.6%
 
483111.6%
 
505023.1%
 
559511.6%
 
ValueCountFrequency (%) 
550592011.6%
 
120858311.6%
 
81204311.6%
 
58020911.6%
 
35471511.6%
 

B5.0 Total Operating Expenditures
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6874367.547
Minimum13210
Maximum211435269
Zeros0
Zeros (%)0.0%
Memory size512.0 B
2022-10-26T16:57:02.741008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13210
5-th percentile24495.75
Q1336452.75
median961445.5
Q32996606.5
95-th percentile19913567.2
Maximum211435269
Range211422059
Interquartile range (IQR)2660153.75

Descriptive statistics

Standard deviation27175024.89
Coefficient of variation (CV)3.95309455
Kurtosis53.05099397
Mean6874367.547
Median Absolute Deviation (MAD)927874.5
Skewness7.068651502
Sum439959523
Variance7.384819775e+14
MonotocityNot monotonic
2022-10-26T16:57:02.984848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
672310423.1%
 
56052423.1%
 
265349723.1%
 
41587723.1%
 
313186023.1%
 
1321023.1%
 
176181923.1%
 
69306323.1%
 
55913923.1%
 
33852523.1%
 
Other values (44)4468.8%
 
ValueCountFrequency (%) 
1321023.1%
 
2194011.6%
 
2289011.6%
 
3359511.6%
 
10513611.6%
 
ValueCountFrequency (%) 
21143526911.6%
 
5079517211.6%
 
3423810111.6%
 
2166571311.6%
 
998474111.6%
 

B4.02.1 Total funds (not including employee benefits)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3916272.531
Minimum0
Maximum133180748
Zeros6
Zeros (%)9.4%
Memory size512.0 B
2022-10-26T16:57:03.420983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1164368
median510228.5
Q31725146
95-th percentile10394188
Maximum133180748
Range133180748
Interquartile range (IQR)1560778

Descriptive statistics

Standard deviation16928962.63
Coefficient of variation (CV)4.322723327
Kurtosis56.28059538
Mean3916272.531
Median Absolute Deviation (MAD)496640.5
Skewness7.340970629
Sum250641442
Variance2.865897756e+14
MonotocityNot monotonic
2022-10-26T16:57:03.923685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
069.4%
 
180213523.1%
 
386300123.1%
 
21833923.1%
 
39749323.1%
 
37404123.1%
 
16436823.1%
 
36799523.1%
 
147775623.1%
 
97765523.1%
 
Other values (40)4062.5%
 
ValueCountFrequency (%) 
069.4%
 
1209811.6%
 
5173811.6%
 
5828611.6%
 
6143911.6%
 
ValueCountFrequency (%) 
13318074811.6%
 
2814200211.6%
 
1452908511.6%
 
1134649611.6%
 
499777611.6%
 
Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729642.5781
Minimum0
Maximum22904129
Zeros7
Zeros (%)10.9%
Memory size512.0 B
2022-10-26T16:57:04.182450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127210.5
median82310
Q3390700
95-th percentile2923175.15
Maximum22904129
Range22904129
Interquartile range (IQR)363489.5

Descriptive statistics

Standard deviation2944801.368
Coefficient of variation (CV)4.035950555
Kurtosis53.0830895
Mean729642.5781
Median Absolute Deviation (MAD)76410
Skewness7.061870219
Sum46697125
Variance8.671855097e+12
MonotocityNot monotonic
2022-10-26T16:57:04.508524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0710.9%
 
4845823.1%
 
13786323.1%
 
5698923.1%
 
50481923.1%
 
7164223.1%
 
52672523.1%
 
39070023.1%
 
3359323.1%
 
8231023.1%
 
Other values (39)3960.9%
 
ValueCountFrequency (%) 
0710.9%
 
589811.6%
 
590211.6%
 
595911.6%
 
712311.6%
 
ValueCountFrequency (%) 
2290412911.6%
 
454421911.6%
 
429585511.6%
 
323359411.6%
 
116413511.6%
 
Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340091.0156
Minimum0
Maximum11886113
Zeros7
Zeros (%)10.9%
Memory size512.0 B
2022-10-26T16:57:04.890475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14611.5
median23844
Q3105737
95-th percentile1068024.35
Maximum11886113
Range11886113
Interquartile range (IQR)101125.5

Descriptive statistics

Standard deviation1513175.541
Coefficient of variation (CV)4.449325243
Kurtosis56.08971859
Mean340091.0156
Median Absolute Deviation (MAD)21631.5
Skewness7.315877437
Sum21765825
Variance2.289700217e+12
MonotocityNot monotonic
2022-10-26T16:57:05.205586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0710.9%
 
1312823.1%
 
1782823.1%
 
5993323.1%
 
26460023.1%
 
272023.1%
 
660223.1%
 
4302123.1%
 
3616323.1%
 
1258423.1%
 
Other values (39)3960.9%
 
ValueCountFrequency (%) 
0710.9%
 
193911.6%
 
227211.6%
 
228011.6%
 
272023.1%
 
ValueCountFrequency (%) 
1188611311.6%
 
222636911.6%
 
161681411.6%
 
115083511.6%
 
59876411.6%
 

P2.2 Resident Households Served
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48232.0625
Minimum0
Maximum1222235
Zeros3
Zeros (%)4.7%
Memory size512.0 B
2022-10-26T16:57:05.486111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile653.55
Q13461.25
median9889
Q325370.5
95-th percentile165228.9
Maximum1222235
Range1222235
Interquartile range (IQR)21909.25

Descriptive statistics

Standard deviation162099.748
Coefficient of variation (CV)3.360829696
Kurtosis45.42732417
Mean48232.0625
Median Absolute Deviation (MAD)8355.5
Skewness6.447624672
Sum3086852
Variance2.627632829e+10
MonotocityNot monotonic
2022-10-26T16:57:05.826819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
034.7%
 
3467623.1%
 
468523.1%
 
354123.1%
 
2261423.1%
 
526823.1%
 
5038823.1%
 
754423.1%
 
1152223.1%
 
397923.1%
 
Other values (43)4367.2%
 
ValueCountFrequency (%) 
034.7%
 
62711.6%
 
80411.6%
 
146511.6%
 
149811.6%
 
ValueCountFrequency (%) 
122223511.6%
 
42232711.6%
 
23720011.6%
 
17685911.6%
 
9932511.6%
 

C0.2.T Total Print Volumes Held
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258622.5781
Minimum0
Maximum9135960
Zeros6
Zeros (%)9.4%
Memory size512.0 B
2022-10-26T16:57:06.207448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120059
median50706.5
Q3149485
95-th percentile539199.05
Maximum9135960
Range9135960
Interquartile range (IQR)129426

Descriptive statistics

Standard deviation1146854.751
Coefficient of variation (CV)4.434472657
Kurtosis59.55479566
Mean258622.5781
Median Absolute Deviation (MAD)38371.5
Skewness7.613985439
Sum16551845
Variance1.31527582e+12
MonotocityNot monotonic
2022-10-26T16:57:06.459507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
069.4%
 
17840023.1%
 
4973323.1%
 
1747123.1%
 
19216723.1%
 
3571223.1%
 
14948523.1%
 
4944323.1%
 
6641523.1%
 
2196023.1%
 
Other values (40)4062.5%
 
ValueCountFrequency (%) 
069.4%
 
540111.6%
 
892611.6%
 
1042911.6%
 
1084811.6%
 
ValueCountFrequency (%) 
913596011.6%
 
146807611.6%
 
67237211.6%
 
58292311.6%
 
29143011.6%
 

D1.1.1.C Librarians
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.96875
Minimum0
Maximum447
Zeros19
Zeros (%)29.7%
Memory size512.0 B
2022-10-26T16:57:06.767082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile41.25
Maximum447
Range447
Interquartile range (IQR)6

Descriptive statistics

Standard deviation56.82833207
Coefficient of variation (CV)4.381943677
Kurtosis56.3183857
Mean12.96875
Median Absolute Deviation (MAD)2
Skewness7.338250643
Sum830
Variance3229.459325
MonotocityNot monotonic
2022-10-26T16:57:06.859166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
01929.7%
 
11218.8%
 
257.8%
 
346.2%
 
546.2%
 
746.2%
 
434.7%
 
1034.7%
 
623.1%
 
8611.6%
 
Other values (7)710.9%
 
ValueCountFrequency (%) 
01929.7%
 
11218.8%
 
257.8%
 
346.2%
 
434.7%
 
ValueCountFrequency (%) 
44711.6%
 
8611.6%
 
5711.6%
 
4511.6%
 
2011.6%
 
Distinct48
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6646.96875
Minimum0
Maximum222159
Zeros8
Zeros (%)12.5%
Memory size512.0 B
2022-10-26T16:57:07.517087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1108.75
median514
Q31816
95-th percentile24949.75
Maximum222159
Range222159
Interquartile range (IQR)1707.25

Descriptive statistics

Standard deviation28788.71799
Coefficient of variation (CV)4.33110476
Kurtosis51.86026937
Mean6646.96875
Median Absolute Deviation (MAD)507
Skewness6.973048291
Sum425406
Variance828790283.6
MonotocityNot monotonic
2022-10-26T16:57:07.797028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
0812.5%
 
25823.1%
 
17023.1%
 
176023.1%
 
47023.1%
 
236223.1%
 
4823.1%
 
88223.1%
 
26823.1%
 
66023.1%
 
Other values (38)3859.4%
 
ValueCountFrequency (%) 
0812.5%
 
1411.6%
 
1711.6%
 
4511.6%
 
4823.1%
 
ValueCountFrequency (%) 
22215911.6%
 
5578111.6%
 
3734211.6%
 
2742711.6%
 
1091211.6%
 
Distinct48
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68310.95312
Minimum0
Maximum1883890
Zeros7
Zeros (%)10.9%
Memory size512.0 B
2022-10-26T16:57:08.079382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14191.5
median18591
Q335250
95-th percentile306294.15
Maximum1883890
Range1883890
Interquartile range (IQR)31058.5

Descriptive statistics

Standard deviation244171.177
Coefficient of variation (CV)3.574407409
Kurtosis50.37743428
Mean68310.95312
Median Absolute Deviation (MAD)15356
Skewness6.832589848
Sum4371901
Variance5.961956366e+10
MonotocityNot monotonic
2022-10-26T16:57:08.367774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
0710.9%
 
1350034.7%
 
610023.1%
 
2000023.1%
 
3500023.1%
 
4460023.1%
 
656223.1%
 
5350023.1%
 
2850023.1%
 
844023.1%
 
Other values (38)3859.4%
 
ValueCountFrequency (%) 
0710.9%
 
75011.6%
 
204811.6%
 
240011.6%
 
242511.6%
 
ValueCountFrequency (%) 
188389011.6%
 
42416411.6%
 
37957411.6%
 
33786611.6%
 
12738711.6%
 

Actual incidents
Real number (ℝ≥0)

HIGH CORRELATION

Distinct52
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5588.09375
Minimum32
Maximum116414
Zeros0
Zeros (%)0.0%
Memory size512.0 B
2022-10-26T16:57:08.698425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile109.35
Q1380
median948.5
Q34830.75
95-th percentile26612.3
Maximum116414
Range116382
Interquartile range (IQR)4450.75

Descriptive statistics

Standard deviation15796.78656
Coefficient of variation (CV)2.826864985
Kurtosis39.69311527
Mean5588.09375
Median Absolute Deviation (MAD)836
Skewness5.908001151
Sum357638
Variance249538465.5
MonotocityNot monotonic
2022-10-26T16:57:08.865018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
41434.7%
 
248223.1%
 
531023.1%
 
46423.1%
 
42623.1%
 
10823.1%
 
313323.1%
 
39023.1%
 
94223.1%
 
205523.1%
 
Other values (42)4367.2%
 
ValueCountFrequency (%) 
3211.6%
 
4711.6%
 
10823.1%
 
11711.6%
 
12011.6%
 
ValueCountFrequency (%) 
11641411.6%
 
3941211.6%
 
2931511.6%
 
2840311.6%
 
1646511.6%
 

Interactions

2022-10-26T16:56:43.491155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:43.639824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.425626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.504899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.599330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.788021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.882421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:44.976827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.061285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.133484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.227970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.322357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.416677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.503398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.582189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.662920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.763343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:45.963790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.053622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.148016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.226803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.305619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.384231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.478479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.566295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.651118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.729220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.816966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:46.895755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.068164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.179861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.268137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.337259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.415365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.509744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.604189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.680151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.769411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.846801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:47.931589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.010474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.183542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.277701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.355838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.434474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.513290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.592024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.686344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.773659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.872580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:48.953961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.050759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.150569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.535105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.644993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.739265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.825788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.904944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:49.999382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:50.108876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:51.401218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:51.510921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:51.619442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:51.714010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:51.839713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.059529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.169108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.247828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.330170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.424820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.518062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.612571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.691258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.780087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.849333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:52.928239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.006854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.205123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.349167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.475546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.569988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.664426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.743054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.841063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.920037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:53.999767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.076433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.145457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.222324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.371187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.455177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.527188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.595195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.663193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.735178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.819210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.887191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:54.959196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.031194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.099187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.167181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.315823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.395822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.779566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.884302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:55.948385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.022408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.105348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.177780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.253777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.330494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.403897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.475917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.635906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.723913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.799907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.871899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:56.943904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.019899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.107905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.179914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.271899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.355907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.443908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.531905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.719820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.823903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.915896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:57.999886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.087906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.175910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.278033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.366029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.444581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.522391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.598646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.670663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.834563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:58.922652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:59.003465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:59.100222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:59.222223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:59.359306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:56:59.575413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-26T16:57:08.969205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-26T16:57:09.429313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-26T16:57:10.191918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-26T16:57:10.693164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-26T16:56:59.873084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-26T16:57:00.468113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sample

First rows

df_indexB2.1.4 Total Provincial Operating FundingB2.1.1 Public Library Operating Grant (PLOG)B5.0 Total Operating ExpendituresB4.02.1 Total funds (not including employee benefits)B4.04 Facilities/Utilities (Costs related to library facility operation, e.g. insurance, rent, lighting, maintenance, etc.)B4.01.2 Electronic (e.g. electronic subscriptions and other databases, downloadable media, gaming software, Playaway, DVDs, and e-resources)P2.2 Resident Households ServedC0.2.T Total Print Volumes HeldD1.1.1.C LibrariansG1.1.2.W All circulation for E-books, downloadable audio books, music and videoE7.1 In the space provided, please provide the total, combined square footage of all the facilities in your library systemActual incidents
0131426313439607451317286612007679424455975050313500426.0
120132971259114303979419214941939313019927016144222055.0
225886785141915591469161136522721944170340782500414.0
3262718081075018461466402354198438759876454661166516135923562007434.0
430843578056725396741638524158735118172227447854461616360003983.0
543150751140457264368029901824670232492741642207933105199680007696.0
6484462442091923935576224516623789110820314711525188132055.0
760325830582194012098022806275401014750414.0
8741471917191193966143958980010848003270178.0
97931000279391202003867000106377176198958516802773183691443.0

Last rows

df_indexB2.1.4 Total Provincial Operating FundingB2.1.1 Public Library Operating Grant (PLOG)B5.0 Total Operating ExpendituresB4.02.1 Total funds (not including employee benefits)B4.04 Facilities/Utilities (Costs related to library facility operation, e.g. insurance, rent, lighting, maintenance, etc.)B4.01.2 Electronic (e.g. electronic subscriptions and other databases, downloadable media, gaming software, Playaway, DVDs, and e-resources)P2.2 Resident Households ServedC0.2.T Total Print Volumes HeldD1.1.1.C LibrariansG1.1.2.W All circulation for E-books, downloadable audio books, music and videoE7.1 In the space provided, please provide the total, combined square footage of all the facilities in your library systemActual incidents
543342092119321998956267242372441137843913401712488397942.0
553445899054533242375216399555062029860177066460532059270002482.0
5634613176112708227366311257242339522101421240148962831068251435310.0
5734751402507983148506188682792368948611486270657101984172022613.0
58361227583220732672310438630015267252646005038814948572362535008117.0
5936222758322073267231043863001526725264600503881494857236253500253.0
603649180790405190755194606527572036950196951276012582281004741.0
61366557392755059202114352691331807482290412911886113122223591359604472221591883890116414.0
62394361881354715807607845934677121895799959932525809020972410388716465.0
6339660168548602612512171029569078101592183477744585489242003133.0